Model Aim

Relatively simple model that models trends in RR-TB prevalence over time, addressing potential bias in who gets tested as well as varied roll-out of share of population being tested

Model Specification

State-level hierarchical generalized additive model (GAM) that models the prevalence of RR-TB positive cases per quarter among incident TB cases between 2014-2019.
Models risk of positivity by patient and municipality characteristics (currently, HIV status, age category, sex, and health unit)
Separate models are run for new TB cases, re-entry cases, and relapsed cases

result ~ s(state, bs = "re") + s(time) + s(time, by = state, id = 1) + age_cat + hiv_status + sex

Model includes:

Random intercept for each state
A different smooth function for time by state with a shared smoothing parameter
Each state-level smoothing parameter varies around a grand smooth function for time to allow for pooling across states
Fixed effects for patient-level characteristics (e.g. age, HIV status, sex) and municipality-level characteristics (e.g. health unit of diagnosis/treatment, FHS coverage by enrollment)

New Cases

Adjusted Model

Same scale

Varied scale

Comparing Models

Adjusted - HIV status, sex, age
Adjusted+ - HIV status, sex, age, health unit
Post 2015 adjusted - HIV status, sex, age, health unit

## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?

Re-Entry Cases

Adjusted Model

Same scale

Varied scale

### Post 2015

Relapse Cases

Adjusted Model

Post 2015

Going Forward:

Individual- and municipality-level covariates to add:
- Add health unit as another covariate
- Exploring covariates for: urbanicity, Bolsa Familia Coverage, FHS Coverage, Household crowding, Treatment abadonment rate per municipality
Come up with inclusion rules for observations (e.g. after certain date, above a certain percent tested)